Top Machine Learning Jobs in Calgary

Leads advanced data analytics for Tribal healthcare initiatives, including data resource inventories, quality assessment, linkage analysis, predictive and prescriptive modeling, reporting, dashboards, and large-scale analytics projects. Establishes data quality frameworks, supports federal compliance, coordinates cross-functional initiatives, and mentors junior analysts. Uses Python, R, SQL, statistical methods, machine learning, and data modeling to produce actionable insights and maintain data integrity.

Leads product strategy, vision, roadmap, and execution for 1Password’s Developer Experience, AI security, and AI-powered security portfolios. Oversees secure developer and AI-agent workflows involving identity, delegation, secrets delivery, and access control. Partners with engineering, design, marketing, partnerships, security, and GTM teams; leads standards and developer engagement; establishes product metrics and operating rhythms; and recruits and mentors senior product managers.

Develop and productionize fraud detection machine learning models using large-scale behavioral, device, network, and transaction data. Lead investigations into complex fraud attacks, reconstruct attacker behavior, identify trends, and produce technical reports and customer-facing insights. Collaborate with product, engineering, operations, legal, executives, and customers to improve detection systems and automate analytic workflows.

Design and operate large-scale data and ML platforms supporting audience data, feature stores, training pipelines, model serving, monitoring, and LLM-powered workflows. Own end-to-end data pipelines, reliability, SLAs, observability, and business impact. Apply distributed systems principles, production ML practices, and agentic technologies while collaborating across product and engineering teams in a fully remote environment.

Own the technical side of deal progression for airline deployments: qualify opportunities, run demos and POCs, author integration and deployment architectures (PSS, APIs, data feeds, DNS, identity, networking), respond to RFPs/security questionnaires, scope SOWs and NFRs, and build reusable demo/reference assets to drive adoption and expansion while liaising between sales, engineering, and customers.

Leads the transformation of Sophos’s pricing operations by building a centralized function, establishing governance and segment-based pricing cadences, and developing AI-assisted competitive and discount analysis. The role builds a new team, partners with Network Security, provides leadership with market intelligence, and tracks pricing performance metrics. Candidates need experience transforming pricing, revenue operations, or deal desk functions in B2B SaaS or technology businesses, along with strong change-management and people-leadership skills.

Annotate robot episodes, videos, images, and sensor data for robotics AI models. Label objects, actions, task steps, and outcomes; write behavioral descriptions; identify ambiguous cases; perform annotation quality checks; meet accuracy and throughput goals; and report tool or workflow issues to engineering. The role requires high attention to detail, strong written communication, sound judgment, and comfort working under deadlines and ambiguity.

Own post-sale relationships with electric utility customers, drive adoption and ROI of the Buzz Solutions platform, identify expansion and at-risk accounts, coordinate training and enablement, advocate customer needs to product and engineering, and maintain account health data in Salesforce.

Lead the design and deployment of large-scale bioinformatics and omics pipelines spanning genomics, transcriptomics, and proteomics. Integrate multimodal biological data with machine learning, develop reproducible analysis frameworks, benchmark predictive models, and collaborate with wet-lab scientists to guide molecular engineering and experimental campaigns. Contribute novel computational biology algorithms, research roadmaps, and scientific leadership.

Designs and implements enterprise cloud and AI solutions for unstructured data workloads. Responsibilities include building ingestion pipelines, API integrations, document extraction and HITL workflows, tuning machine learning models, benchmarking performance, and ensuring security, availability, disaster recovery, and data isolation. The role also supports customer developers, leads technical proofs of concept, mentors engineering teams, develops reusable blueprints, and provides feedback to product engineering.

Build scalable, low-latency backend APIs, event-driven services, integration layers, and microservices supporting personalization and recommendation experiences. Partner with AI/ML and data engineers to deploy models, expose AI services, and develop React applications. Improve security, observability, reliability, performance, scalability, and operational efficiency while using AI-assisted engineering tools to enhance delivery and code quality.

Leads Kinaxis’s Advanced Mathematical Modelling function, managing architects, algorithm developers, and operations research specialists. Provides technical and strategic leadership across optimization, machine learning, simulation, decision science, and algorithm development. Partners with Product Management, Engineering, and customer teams to solve supply chain challenges, guides research and prototyping, shapes platform architecture, and promotes scalable production solutions. Oversees talent acquisition, coaching, workforce planning, performance management, and career development while establishing the function’s vision and operating model.

Own post-sale relationships for electric utility customers, drive platform adoption, retention, ROI, and expansion opportunities. Build onboarding, health scoring, QBR, escalation, and customer success playbooks from scratch. Serve as a bridge between customers and internal teams, train customers, influence product priorities, and eventually lead hiring and development of the Customer Success team.

Build and maintain mission-critical wealth management systems for SMB clients. Design data pipelines, ETL/ELT processes, data models, warehouses, analytics, and reporting solutions using AWS, Snowflake, Mage AI, and other big data tools. Collaborate with data scientists and analysts on predictive models, statistical analyses, and machine learning algorithms. Develop software with Next.js and Python while working closely with the team and clients on financial-domain challenges.

Build and deploy LLM-powered features for clinical, patient, and operational workflows. Responsibilities include prompt design, retrieval, tool use, agent orchestration, evaluation harnesses, feedback loops, production operations, and AI engineering tooling. Partner with clinical, product, and safety stakeholders to ensure responsible healthcare-grade AI. Mentor teammates, communicate model behavior clearly, and stay current with emerging AI research and development practices.

Design scalable human-data solutions for frontier AI clients, translating ambiguous model-training and evaluation needs into executable programs. Lead discovery, methodology, task and workflow design, feasibility estimates, expert profiling, quality strategies, and technical architecture in partnership with engineering and project teams. Advise on fine-tuning, RLHF, benchmarks, red teaming, multimodal data, coding tasks, and agent environments while guiding pilots through early delivery and developing reusable best practices.

Owns the strategy and roadmap for semantic insurance data products supporting AI, analytics, automation, and enterprise applications. Defines canonical concepts, relationships, metadata, data quality standards, APIs, knowledge graphs, and AI-ready consumption patterns. Partners with engineering, architecture, data, AI, UX, customer success, and insurance stakeholders to translate complex P&C insurance needs into product capabilities, prioritize use cases, establish requirements, drive adoption, and measure business impact.

Leads lab-in-the-loop biology programs that connect experimental design with machine learning model development. Oversees closed-loop experiments, model-output verification, perturbation studies, data generation, assay and model-system development, automation, metadata standards, CRO and vendor relationships, budgets, and cross-functional collaboration. Builds and mentors technical teams while aligning experimental and computational groups across a matrixed biotech organization.

Build and ship production AI systems for enterprise portfolio and planning workflows. Develop anomaly and signal detection, LLM-powered insight synthesis, natural-language planning rule translation, evaluation frameworks, and MCP tools. Partner with external AI providers initially, then establish internal standards for prompt design, model selection, agent architecture, reliability, and evaluation. Collaborate closely with product, design, and domain engineering teams.

Design and lead end-to-end architectures for production machine-learning systems at extraordinary scale. Responsibilities span data preparation, feature generation, model delivery, deployment, hosting, inference, monitoring, performance optimization, and technical design. The role establishes engineering standards, influences ML architecture, and collaborates across data science and software engineering to build reliable, maintainable systems for high-volume, low-latency workloads.

Design, develop, optimize, and deploy scalable computer vision and vision-language systems using classical computer vision and deep learning. Build evaluation frameworks, monitor production models, debug issues, and improve architectures with new data. Collaborate with product managers and cross-functional teams to deliver practical AI solutions across cloud and edge environments.

Own the ML Platform product roadmap supporting model training, evaluation, deployment, serving, monitoring, and AI capabilities. Translate the needs of applied scientists and ML engineers into priorities, proposals, and platform capabilities while balancing reliability, cost, scalability, and technical debt. Lead discovery, evaluate build-versus-buy options, coordinate cross-functional dependencies, drive platform adoption, and support migrations from legacy tools. The role requires technical product management experience and practical understanding of production machine learning systems.

Design, build, evaluate, and deploy LLM-powered agents and machine learning systems that improve Quora content quality and safety. The role involves developing agentic workflows, conducting offline and online evaluations, monitoring production performance, investigating failures, and improving accuracy, reliability, latency, and cost. New graduates will collaborate with experienced engineers and own projects from development through production.

Designs benchmarks, reinforcement learning environments, evaluation methodologies, scoring frameworks, and reproducible evaluation architectures for frontier models. Builds and ships production code, maintains data pipelines, analyzes results, and collaborates with research scientists, solutions architects, and ML engineers. The role combines independent research methodology with hands-on engineering across agentic systems, model evaluation, and RL infrastructure.

Design, build, deploy, and optimize scalable AI and machine learning systems using large datasets. Develop predictive models, recommendation systems, personalization engines, forecasting solutions, generative AI applications, and production ML pipelines. Collaborate with data, backend, and product teams to integrate models into applications, while monitoring performance, data quality, drift, reliability, and cost. Establish best practices for evaluation, experimentation, reproducibility, and deployment, and research emerging AI technologies.